ABSTRACT Early detection of multiple sclerosis is key to limiting neurological damage but monitoring patient progression and response to therapy is of arguably similar if not greater importance due to the chronic nature of disease. Moreover, rates of non-adherence to therapy has been reported to be as high as 25% to 40% in the patient population suggesting the need to provide continuous monitoring and selection of optimal therapy. Identification of novel actionable biomarkers would provide clinicians with additional information for the purposes of diagnosis, prognosis, clinical subtyping as well as for the selection and monitoring of therapy. Initiation of sub-optimal therapy can be both detrimental to the patient?s health and financial well-being. To date, the general approach to selecting a disease modifying treatment (DMT) is to weigh the risks and benefits while considering the aggressiveness of disease, efficacy of the drug and the potential side effects of treatment in a ?trial and error? fashion. This approach is quite unsettling when understanding that treatment failure or inadequacy can cause irreversible neurological damage. Furthermore, many of these drugs are associated with serious adverse drug reactions such as cardiac events, opportunistic infections and secondary autoimmunity. Selection of the best therapy for a particular patient as well as the ability to identify if/when efficacy of a particular DMT dwindles is highly desirable and would be of great benefit throughout the healthcare spectrum. The course of MS disease does not manifest identically in all patients nor do all patients respond to treatment the same way. Identification of actionable biomarkers to serve as a surrogate for the efficacy of a particular therapy would allow clinicians to identify nonresponsive patients as early as possible and potentially evaluate dosing or administration to optimize patient outcomes. Our previous work has explored lncRNAs as candidate biomarkers that can be measured in peripheral whole blood to accurately classify MS. The preliminary data provided in support of our fast track application highlights the potential for lncRNA expression levels analyzed with machine learning to not only classify MS but also indicate treatment responses.